Objective: In recent years, ensemble learning methods have gained widespread use for early diagnosis of cancer diseases. this study, it is aimed to establish a high-performance model and classification renal cell carcinomas.Methods: the hemogram laboratory data 140 patients with carcinoma without were included in study. The set includes 27 predictors 1 dependent variable. obtained retrospective...

Employees are the most valuable resources for any organization. The cost associated with professional training, developed loyalty over years and sensitivity of some organizational positions, all make it very essential to identify who might leave Many reasons can lead employee attrition. In this paper, several machine learning models automatically accurately predict IBM attrition dataset is used...

Abstract Having the option of a temporally unmanned bridge when conditions allow, while maintaining or even enhancing navigational safety, is long term aim in maritime industry. Such system requires excellent perception environment using an array sensors. This paper investigates performance object detection at sea electro-optical sensors relevant spectral ranges and discusses how missed risk mi...

Conformal prediction is a popular tool for providing valid sets classification and regression problems, without relying on any distributional assumptions the data. While traditional description of conformal starts with nonconformity score, we provide an alternate (but equivalent) view that sequence nested calibrates them to find set. The framework subsumes all scores, including recent proposals...

One of the most important issues concerning the sensor data in the Wireless Sensor Networks (WSNs) is the unexpected data which are acquired from the sensors. Today, there are numerous approaches for detecting anomalies in the WSNs, most of which are based on machine learning methods. In this research, we present a heuristic method based on the concept of “ensemble of classifiers” of data minin...